Founding AI Engineer - Business Automation

Founding AI Engineer. Business Automation

Location
:
Metro DC;
Atlanta, GA;
Raleigh, NC;
Burlington, VT; or Remote/Hybrid

Employment Type
:
Full time. Mostly remote, though being closer to a physical office location is preferred.

Team
:
Business Automation, AI-centric solutions

Reports To
:
Chief Innovation Officer

Travel
: 0–10%. Occasional client workshops and internal collaboration.

Compensation
: $140,000–$175,000 plus bonus

About Our Client

The company we are recruiting for is a public accounting firm specializing in audit, tax, and advisory services for the insurance and nonprofit sectors, as well as employee benefit plans. For more than 30 years, they have combined deep industry expertise with a people-first culture built on agility, respect, and trust.

They are making a significant investment in technology to modernize how assurance and tax services are delivered. The goal is to free their teams to focus on high-value judgment while providing clients with faster and more insightful outcomes.

About the Team and Role

You will join the Business Automation team, which is responsible for building the next generation of tools that support how the firm works across data, automation, and intelligent systems.

This is the team’s first AI engineering hire and a true greenfield role. There is no legacy codebase and no prebuilt platform.

In this role, you will:

• Act as a founding AI engineer and the primary builder of the firm’s AI stack on AWS
• Design and deliver a vision for workforce automation systems that can reason, use tools, and collaborate with people
• Lay the foundation to grow into a technical leadership or management role by mentoring future AI and application engineers

The firm is deeply invested in AWS and works with multiple model providers. These include AWS Bedrock as the primary platform, as well as OpenAI, Gemini, Grok, and others. You will be expected to select and combine the right tools for each use case, balancing risk, cost, performance, and long-term maintainability.

You will also own key technical relationships with AI vendors and partners. This includes working with external partners who have delivered proofs of concept and turning successful experiments into stable, internal capabilities.

What You’ll Do

• Design the AI and automation foundation on AWS
  • Define reference architectures for LLM and agent-based workloads, including orchestration, retrieval, tools, evaluation, and guardrails
  • Use AWS Bedrock alongside external providers such as OpenAI, Gemini, and Grok in a modular, provider-agnostic way
• Build AI products that support core audit and tax workflows
  • Deliver copilots and automated helpers that assist with drafting work papers, analyzing documents, summarizing findings, generating testing selections, and preparing client deliverables
  • Own the full lifecycle from discovery with domain experts through prototyping, production, monitoring, and iteration based on feedback
• Implement robust retrieval and tool-using agents
  • Design retrieval pipelines over internal content, structured data, and work papers with appropriate metadata and access controls
  • Build agents that can call tools such as internal APIs, calculators, automations, and workflows while operating within clearly defined boundaries
• Establish safety, privacy, and governance practices
  • Partner with risk, security, and data teams to ensure responsible handling of client data, including PII controls, redaction strategies, training boundaries, and access management
  • Define patterns for prompt hardening, guardrails, and evaluation that align with professional and regulatory obligations
• Own vendor and partner relationships
  • Serve as the hands-on technical counterpart for AWS, OpenAI, Google, xAI, and other partners
  • Evaluate new capabilities, run proofs of concept, and make informed build-versus-buy decisions
• Collaborate across teams
  • Work closely with data engineering and automation leads to connect AI systems to well-governed data and existing workflows
  • Translate loosely defined business problems into clear engineering deliverables and roadmaps
• Set engineering standards and mentor future hires
  • Establish best practices for AI application development, including testing,…